Our goal is to learn an embedding space that groups image patches containing the same part of an object.
This is motivated by the hypothesis that object part representations are more general than object representations, as parts can be recombined in multiple ways to different objects.
As an example, a wheel representation can be combined to a car representation but also a bus representation.
Therefore, object part representations should transfer better across datasets.
For that, we aim to design a pretext task that allows for intra-image category learning on an image-patch-level.
Thus, a clustering pretext task is a natural choice.
As shown in Figure REF , we retrieve patch-level optimal cluster assignments from a teacher network and predict them from the student network.
The choice of a clustering pretext task is further supported by empirical evidence showing that clustering pretext outperforms contrastive pretext for dense prediction tasks [1]}, [2]}.
Instead of pretraining models from scratch which requires substantial GPU budgets, we use our loss to fine-tune pretrained neural networks.
Further, this circumvents known cluster stability issues and clusters capturing low-level image features when applied to a patch-level as reported in [3]}.
